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Geometric attitude tracking control of quadrotor UAVs with adaptive extended state observers

ISA Transactions 2025年 160卷 289-297页

作者： Wang, Liping Chen, Jiapeng Huang, Yuyuan Zhu, Yongbin Pei, Hailong Key Laboratory of Autonomous Systems and Networked Control Ministry of Education Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong School of Automation Science and Engineering South China University of Technology Guangzhou510640 China

This paper studies a geometric attitude tracking control problem of quadrotor unmanned aerial vehicles (UAVs) with adaptive extended state observers (AESOs). Through coordinate transformation, the error dynamic of AESO can be transformed into a canonical form which is easier to analyze by linear time-varying theory. In the presence of unknown disturbances and system uncertainties, the estimated error and stability of AESO can be analyzed. Simultaneously, a geometric tracking controller is developed on SO(3) with AESOs which guarantees that the error exponentially converges to a bounded set related to the estimated error. Some examples and a flight test are given to verify the availability of the control scheme. © 2025

关键词： Unmanned aerial vehicles (UAV)

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Fast Iterative Region Inflation for Computing Large 2-D/3-D Convex Regions of Obstacle-Free Space

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IEEE Transactions on robotics 2025年 41卷 3223-3243页

作者： Wang, Qianhao Wang, Zhepei Wang, Mingyang Ji, Jialin Han, Zhichao Wu, Tianyue Jin, Rui Gao, Yuman Xu, Chao Gao, Fei Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Hangzhou310027 China Zhejiang University Huzhou Institute Huzhou313000 China

Convex polytopes have compact representations and exhibit convexity, which makes them suitable for abstracting obstacle-free spaces from various environments. Existing generation methods struggle with balancing high-quality output and efficiency. Moreover, another crucial requirement for convex polytopes to accurately contain certain seed point sets, such as a robot or a front-end path, is proposed in various tasks, which we refer to as manageability. In this paper, we propose Fast Iterative Regional Inflation (FIRI) to generate high-quality convex polytope while ensuring efficiency and manageability simultaneously. FIRI consists of two iteratively executed submodules: Restrictive Inflation (RsI) and Maximum Volume Inscribed Ellipsoid (MVIE) computation. By explicitly incorporating constraints that include the seed point set, RsI guarantees manageability. Meanwhile, iterative MVIE optimization ensures high-quality result through monotonic volume bound improvement. In terms of efficiency, we design methods tailored to the low-dimensional and multi-constrained nature of both modules, resulting in orders of magnitude improvement compared to generic solvers. Notably, in 2-D MVIE, we present the first linear-complexity analytical algorithm for maximum area inscribed ellipse, further enhancing the performance in 2-D cases. Extensive benchmarks conducted against state-of-the-art methods validate the superior performance of FIRI in terms of quality, manageability, and efficiency. Furthermore, various real-world applications showcase the generality and practicality of FIRI. The high-performance code of FIRI will be open-sourced. © 2004-2012 IEEE.

关键词： Efficiency

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Self-adjusting voxelated electrochemical three-dimensional printing of metallic microstructures

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International Journal of Extreme Manufacturing 2025年第1期7卷 420-433页

作者： Xianghe Meng Xiaomo Wu Xingjian Shen Yan Xu Hao Zhang Mingjun Chen Hui Xie State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin 150080People’s Republic of China

Microscale metallic structures enhanced by additive manufacturing technology have attracted extensive attention especially in microelectronics and electromechanical ***-confined electrodeposition(MCED)advances microscale 3D metal printing,enabling simpler fabrication of superior metallic microstructures in air without complex equipment or ***,accurately predicting growth rates with current MCED techniques remain challenging,which is essential for precise structure fabrication and preventing nozzle *** this work,we present a novel approach to electrochemical 3D printing that utilizes a self-adjusting,voxelated method for fabricating metallic *** from conventional voxelated printing which focuses on monitoring voxel thickness for structure control,this technique adopts a holistic *** ensures each voxel’s position is in alignment with the final structure by synchronizing the micropipette’s trajectory during deposition with the intended design,thus facilitating self-regulation of voxel position and reducing errors associated with environmental fluctuations in deposition *** method’s ability to print micropillars with various tilt angles,high density,and helical arrays demonstrates its refined control over the deposition *** electron microscopy analysis reveals that the deposited structures,which are fabricated through layer-by-layer(voxel)printing,contain nanotwins that are widely known to enhance the material’s mechanical and electrical ***,in situ scanning electron microscopy(SEM)microcompression tests confirm this enhancement,showing these structures exhibit a compressive yield strength exceeding 1 *** indentation tests provided an average hardness of 3.71 GPa,which is the highest value reported in previous work using *** resistivity measured by the four-point probe method was(1.95±0.01)×10^(−7)Ω·m,nearly 11 times that of bulk ***

关键词： additive manufacturing self-adjusting voxelated electrodeposition metallic microstructures 3D printing nanotwinned copper

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Post-Stroke Corticomuscular Coupling Assessment Based on Bilateral Cerebral Hemisphere Difference

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IEEE Transactions on Neural systems and Rehabilitation Engineering 2025年 33卷 1817-1826页

作者： Chang, Hui Xin, Rong Sheng, Yixuan Wang, Zhiyong Cao, Ruikai Yu, Xianxian Wang, Pu Liu, Honghai Sun Yat-sen University The Seventh Affiliated Hospital Shenzhen518107 China Harbin Institute of Technology at Shenzhen State Key Laboratory of Robotics and Systems Shenzhen518055 China

Corticomuscular coupling (CMC) can quantify the information interaction between the brain and muscles during motor control. However, current research regarding changes in CMC after stroke is inconsistent. To address this, this paper propose a novel use of CMCdif as an indicator to assess motor function after stroke. This indicator include WCdif, derived from wavelet coherence analysis and TSEdif, derived from transfer spectral entropy analysis. Twelve stroke patients and twelve healthy controls were included in this study, with an experimental paradigm of upper limb isokinetic push-pull movements. The results revealed that WCdif were significantly higher in the stroke patient group compared to the healthy group. Moreover, the TSEdif of stroke group is higher than healthy group on the efferent pathway, but no difference on the afferent pathway. Utilizing the validated CMCdif indices, we developed a motor function assessment model that showed strong relation with clinical assessment outcomes (R2 = 0.873, p = 0.003). These findings provide a new insight to understand the mechanisms underlying CMC changes after stroke. The combined use of linear and nonlinear indicators enhances the potential of CMC for clinical motor function assessment in stroke patients. © 2001-2011 IEEE.

关键词： Stroke (medical condition) Couplings Muscles Motors Force Electroencephalography Electromyography Coherence Robots Motor drives

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E-3DGS: 3D Gaussian splatting with exposure and motion events

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Applied Optics 2025年第14期64卷 3897-3908页

作者： Yin, Xiaoting Shi, Hao Bao, Yuhan Bing, Zhenshan Liao, Yiyi Yang, Kailun Wang, Kaiwei State Key Laboratory of Modern Optical Instrumentation Zhejiang University Hangzhou China The Chair of Robotics AI and Real-Time Systems Technical University of Munich Munich Germany College of Information Science and Electronic Engineering Zhejiang University Hangzhou China School of Robotics and National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha China

Achieving 3D reconstruction from images captured under optimal conditions has been extensively studied in the vision and imaging fields. However, in real-world scenarios, challenges such as motion blur and insufficient illumination often limit the performance of standard frame-based cameras in delivering high-quality images. To address these limitations, we incorporate a transmittance adjustment device at the hardware level, enabling event cameras to capture both motion and exposure events for diverse 3D reconstruction scenarios. Motion events (triggered by camera or object movement) are collected in fast-motion scenarios when the device is inactive, while exposure events (generated through controlled camera exposure) are captured during slower motion to reconstruct grayscale images for high-quality training and optimization of event-based 3D Gaussian splatting (3DGS). Our framework supports three modes: high-quality reconstruction using exposure events, fast reconstruction relying on motion events, and balanced hybrid optimizing with initial exposure events followed by high-speed motion events. On the EventNeRF dataset, we demonstrate that exposure events significantly improve fine detail reconstruction compared to motion events and outperform frame-based cameras under challenging conditions such as low illumination and overexposure. Furthermore, we introduce EME-3D, a real-world 3D dataset with exposure events, motion events, camera calibration parameters, and sparse point clouds. Our method achieves faster and higher-quality reconstruction than event-based NeRF and is more cost-effective than methods combining event and RGB data. By combining motion and exposure events, E-3DGS sets a new benchmark for event-based 3D reconstruction with robust performance in challenging conditions and lower hardware demands. The source code and dataset are available on GitHub. © 2025 Optica Publishing Group.

关键词： Exposure controls

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Human-Robot Pose Tracking Based on CNN with Color and Geometry Aggregation 16th

Human-Robot Pose Tracking Based on CNN with Color and Geo...

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16th International Conference on Social robotics, ICSR + InnoBiz 2024

作者： Xu, Yue Zhang, Yinlong Liu, Shuai Liu, Yuanhao Liang, Wei He, Hongsheng Shenyang University of Technology Shenyang110020 China Guangzhou Institute of Industrial Intelligence Guangzhou511458 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Department of Computer Science The University of Alabama TuscaloosaAL35487 United States

ISBN: (纸本)9789819611508

Accurately tracking the robotic arm and human joints is crucial to ensure safety during human-robot interaction. However, traditional pose tracking methods often exhibit insufficient performance and robustness in complex environments. The variations in the robotic arm’s environment and posture make it challenging for traditional methods to accurately capture the positions and posture of its joints. Specifically, when addressing challenges such as high similarity, occlusion, background complexity, and joint recognition failures, they often struggle to provide reliable and accurate results. To address these challenges, this paper proposes a human-robot pose tracking algorithm based on a new convolutional neural network model. To enhance detection accuracy, an improved color detection module is introduced to resolve joint misclassification. A geometric perception module is designed to accurately locate joints even under occlusion. Additionally, innovative iEMA and DBB modules are incorporated. The iEMA module employs edge detection technology to dynamically adjust thresholds for correct matching by improving the edge matching process. The DBB module refines the boundary box parameters for precise localization by introducing adaptive bounding box updates in real-time. This algorithm also integrates human pose recognition, enabling real-time pose recognition, thereby facilitating more intelligent and natural human-robot interaction. The algorithm has been rigorously evaluated on a custom-designed robotic arm platform. Experimental results validate the algorithm’s effectiveness and feasibility. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Robotic arms

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On-orbit assembly control based on model predictive control and load online identification

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Advances in Space Research 2025年

作者： Li, Yuntao Xu, Zichun Zhao, Zhiyuan Zhuang, Lei Yang, Xiaohang Ma, Boyu Zhao, Jingdong Liu, Hong State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150001 China

Space manipulator are essential for on-orbit assembly (OOA) tasks. This paper proposes an advanced control framework to address key challenges in Cartesian trajectory tracking and compliant assembly. First, a rigid-body dynamic model of the manipulator is established using CAD model and joint friction identification. A Linear Extended state Observer (LESO) is designed to mitigate system errors by modeling both parametric uncertainties and external disturbances as an extended state. Additionally, the method for selecting parameters of the LESO, along with its convergence proof, is presented. Second, a model predictive control (MPC) algorithm is employed to optimize the Cartesian trajectory of the manipulator in real time. By linearizing the Cartesian space trajectory, the MPC problem is formulated as a QP problem. Solving this QP problem in real-time allows for the optimization of the Cartesian space trajectory, thereby improving the trajectory tracking accuracy in Cartesian space. Finally, online identification of load parameters, sensor zero drift, and temperature drift enables effective compliant assembly control. Extensive experiments validate the proposed methods: (1) The Cartesian trajectory tracking error is maintained below 0.1 mm. (2) After sensor compensation for identified load parameters, zero drift, and temperature drift, the maximum force residual is 0.42 N, and the maximum torque residual is 0.1 N m. © 2025 COSPAR

关键词： Predictive control systems

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Data-Driven Tracking control of a Cushion Robot with Safe Autonomous Motion Considering Human-Machine Interaction Environment

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Journal of Dynamic systems, Measurement and control 2025年第4期147卷 041003页

作者： Sun, Ping Shan, Rui Chang, Hongbin Wang, Shuoyu School of Artificial Intelligence Shenyang University of Technology Shenyang110870 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110169 China Department of Intelligent Mechanical Systems Engineering Kochi University of Technology Kochi7828502 Japan

This study proposes a data-driven safety controller with velocity constraints for a cushion robot. We constructed a mathematical description of the human-machine interaction environment by decomposing the generalized input force and coefficient matrix in the dynamic model. We established a new equivalent data model, which considered various human-machine interaction environments using a pseudo-Jacobian matrix. A stochastic configuration network (SCN) estimation method for variations in the human-machine interaction environment was proposed, with hidden layer nodes added at random. We designed a safe autonomous navigation path and proposed a data-driven control method that limited the actual velocity while stabilizing the tracking error system. In addition, the desired motion velocity of the robot was designed. This approach has the advantage of ensuring safety at a specified velocity. We also demonstrated and validated the effectiveness of the proposed data-driven algorithm using simulation-based comparative analysis and an experimental study. Copyright © 2024 by ASME.

关键词： Stochastic systems

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Improving Collision-Free Success Rate For Object Goal Visual Navigation Via Two-Stage Training With Collision Prediction

arXiv

引用

arXiv 2025年

作者： Lian, Shiwei Zhang, Feitian Robotics and Control Laboratory Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory of Turbulence and Complex Systems Peking University Beijing100871 China

The object goal visual navigation is the task of navigating to a specific target object using egocentric visual observations. Recent end-to-end navigation models based on deep reinforcement learning have achieved remarkable performance in finding and reaching target objects. However, the collision problem of these models during navigation remains unresolved, since the collision is typically neglected when evaluating the success. Although incorporating a negative reward for collision during training appears straightforward, it results in a more conservative policy, thereby limiting the agent’s ability to reach targets. In addition, many of these models utilize only RGB observations, further increasing the difficulty of collision avoidance without depth information. To address these limitations, a new concept—collision-free success is introduced to evaluate the ability of navigation models to find a collision-free path towards the target object. A two-stage training method with collision prediction is proposed to improve the collision-free success rate of the existing navigation models using RGB observations. In the first training stage, the collision prediction module supervises the agent’s collision states during exploration to learn to predict the possible collision. In the second stage, leveraging the trained collision prediction, the agent learns to navigate to the target without collision. The experimental results in the AI2-THOR environment demonstrate that the proposed method greatly improves the collision-free success rate of different navigation models and outperforms other comparable collision-avoidance methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Dynamic Blockchain-Empowered Trustworthy End-Edge Collaborative Computing via Rotating Multi-Agent DRL

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IEEE Transactions on Wireless Communications 2025年

作者： Xu, Chi Zhang, Peifeng Yu, Haibin Li, Yonghui Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China University of Chinese Academy of Sciences Beijing China University of Sydney School of Electrical and Information Engineering Sydney Australia

Blockchain-empowered end-edge collaborative computing is a promising technology for enhancing the timeliness and trustworthiness of Industrial Internet of Things (IIoT). However, integrating task offloading with blockchain consensus inevitably escalates resource consumption across communication, computation, and energy domains. Thus, the joint optimization of task offloading, resource allocation and blockchain consensus is very important for IIoT. This paper studies a general end-edge collaborative computing scenario with multiple end devices and multiple edge servers. We first propose a novel dynamic blockchain (DBC) scheme by developing a dynamic leader election mechanism and designing a dynamic consensus waiting time window. Then, by fully considering the constraints of multi-task size and deadline, communication bandwidth, computing frequency, battery capacity, Byzantine fault tolerant and trustworthiness, we formulate the trustworthy processing efficiency (TPE) maximization problem with respect to end-edge task division, communication and computation resource allocation, leader election and consensus waiting window. To address this problem, we transform it into a Markov decision process and design a compound reward by fully considering the penalty for computing timeout and consensus failure. After that, we propose a rotating multi-agent deep reinforcement learning (R-MADRL) algorithm tailored to the proposed DBC scheme, where an entropy-based dual-critic DRL algorithm is proposed for rotating multi-agent training and decentralized execution. Extensive experiments validate the effectiveness and superiority of the proposed DBC with R-MADRL, where three benchmark DRL algorithms and three blockchain consensus schemes are compared. The results demonstrate that R-MADRL achieves stable convergence with more than 60.32% TPE reward than other algorithms while the task timeout ratio of DBC is reduced by more than 66.49% compared with other schemes. © 2025 Institute of El

关键词： Markov processes

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